JP7730702B2 - Molded product removal machine - Google Patents

Description

The present invention relates to a molded product removal machine equipped with a function to determine whether a molded product has been incorrectly picked up by a suction member.

JP 2007-319960 A (Patent Document 1) discloses a molded product removal machine equipped with a vacuum abnormality determination means that determines whether a vacuum abnormality has occurred due to the condition of the piping or suction member before suction, based on the degree of vacuum detected by a vacuum pressure detector before the suction member suctions the molded product.

In addition, Japanese Patent Application Laid-Open No. 1-86088 (Patent Document 2) discloses technology that uses a vacuum pressure detection unit that distinguishes between multiple levels of vacuum pressure inside the nozzle when a component is being picked up, and distinguishes between good and bad component and suction status as differences in vacuum pressure levels.

Japanese Patent Application Laid-Open No. 2007-319960 Japanese Patent Application Publication No. 1-86088

The invention described in Patent Document 1 detects the possibility of pipe damage or clogging by comparing the dry suction pressure state before suction with a specified level range. This makes it possible to detect vacuum abnormalities before a workpiece is suctioned by the suction member, preventing suction errors and workpieces falling during transport. However, this conventional invention does not provide information on suction errors that occur after suction has begun, or their causes. Furthermore, this invention does not provide information on the possibility of a suction error occurring even if one has not actually occurred.

Furthermore, when distinguishing between multiple stages of vacuum pressure inside the nozzle when a part is being picked up and determining whether the part is good, bad, or in the pick-up state, as in the conventional invention shown in Patent Document 2, there is a problem in equipment that frequently changes heads, such as molded product extractors, where the dry suction pressure changes each time the head is changed, making it difficult to set the vacuum pressure stage differences that serve as the basis for judgment. Furthermore, the invention described in this publication does not allow for highly accurate identification of the cause of pick-up errors, let alone the possibility of a pick-up error occurring.

The object of the present invention is to provide a molded product removal machine that can accurately identify the cause of a molded product pickup error and/or the cause of the possibility of a pickup error occurring.

In addition to the above, another object of the present invention is to provide a molded product remover that can identify the cause of a suction error and/or the possible cause of a suction error and how to deal with it.

The molded product removal machine of the present invention comprises a suction member that suctions a molded product; a vacuum generator connected to the suction member via piping and applying vacuum pressure to the suction member; a pressure valve that controls the vacuum pressure applied to the suction member from the vacuum generator; and a pressure detector that detects the pressure acting on the suction member; a movement mechanism that moves the suction member from the molding machine's mold; a control device that controls the operation of the vacuum generator and pressure valve and the drive of the movement mechanism; a pressure data storage unit that stores changes in pressure detected by the pressure detector as time-series pressure data; and a suction failure cause determination unit that determines the occurrence and cause of a failure to suction a molded product by the suction member. The suction failure cause determination unit compares the time-series pressure data with predetermined time-series judgment criteria data and determines the cause of the failure to suction a molded product by the suction member and/or the cause of the possibility of the failure (i.e., the cause of the failure to suction a molded product and at least one cause of the possibility of the failure) based on the time-series comparison results.

The time-series pressure data contains all information about the adsorption status during the adsorption process and the process after adsorption. Even if the pressure does not increase or temporarily fluctuates for some reason during the adsorption operation, these conditions will be reflected in the time-series pressure data. Therefore, as in the present invention, by comparing the time-series pressure data with predetermined time-series judgment criteria data and obtaining the time-series comparison results, even if the pressure temporarily exceeds the threshold, it will not be erroneously determined that no adsorption error has occurred. Furthermore, the pattern of change in the time-series pressure data can also detect the occurrence of some cause that may have caused the adsorption error during the operation. By understanding the characteristics of pressure changes that appear in the time-series pressure data when an adsorption error occurs due to a known cause, it is possible to determine the cause of the adsorption error. Furthermore, by understanding the characteristics of pressure changes that appear in the time-series pressure data during multiple removal processes before the adsorption error occurred, it is possible to determine the causes that may lead to the adsorption error. Therefore, according to the present invention, it is possible to determine with greater accuracy than before whether a molded product has failed to be picked up and/or whether there is a possibility that a pick-up error will occur, and it is also possible to identify the cause of the pick-up error and/or the cause of the possibility of a pick-up error occurring.As a result, the present invention can be used not only to detect the occurrence of pick-up errors, but also in predictive maintenance of molded product removal machines.

The time-series pressure data includes the dry suction pressure, which is the pressure inside the pipe before the suction member picks up the molded product; the suction operation start pressure, which indicates that the suction member has started to pick up the molded product; the post-removal rise pressure, which is the pressure after the suction member has risen to a specified rise position after the suction operation start pressure is detected; and the peak pressure, which is the maximum pressure inside the pipe between the detection of the dry suction pressure and the detection of the post-removal rise pressure. Note that although these pressures are characteristic pressures, not all of them are clearly included in the time-series pressure data; depending on the situation, they may or may not appear in the time-series pressure data. The time-series criteria data is determined on the assumption that the various pressures listed above are included in the pressure data.

The time-series judgment criteria data includes a reference pressure value determined through prior testing and multiple time-series pressure change pattern information related to the causes of pickup errors and/or causes linked to the possibility of a pickup error, which have been collected in advance. The pickup error cause judgment unit judges that there is no pickup error when the dry suction pressure is lower than the reference pressure value and the post-take-up pressure and peak pressure are both lower than the reference pressure value. If this condition is not met, it judges that a pickup error has occurred or is likely to occur. If it judges that a pickup error has occurred or is likely to occur, it can be configured to output information on the cause of the pickup error based on the comparison result and the multiple time-series pressure change pattern information. When the time-series comparison result is used, the trends that appear in the comparison result do not change significantly even if the pickup head used is changed, preventing erroneous judgment of the causes of pickup errors and/or the causes linked to the possibility of a pickup error.

It is preferable that the reference pressure value be changeable. This allows for fine adjustments and increases versatility.

The reference pressure values may include a first reference pressure value used to determine whether a pick-up error has occurred and a second reference pressure value used to determine the possibility of a pick-up error occurring. This can further improve the accuracy of the determination.

The pressure data storage unit stores the time-series change pattern of pressure for each removal cycle, and the pickup error cause determination unit preferably outputs a countermeasure for the cause from the time-series change pattern when it determines that a pickup error has occurred or is likely to occur. If a countermeasure is output, the cause can be corrected immediately after determining that a pickup error has occurred, and the cause of a possible pickup error can be corrected before the pickup error actually occurs, thereby preventing a significant decrease in production efficiency.

The pickup error cause determination unit may acquire the cause of a pickup error or the possibility of a pickup error by inputting the time-series pressure data into a trained learning model that has undergone machine learning using previously collected multiple time-series pressure data when a pickup error occurred or when there was a possibility of a pickup error and the causes of the pickup error as training data. In other words, the pickup error cause determination unit may be configured using so-called artificial intelligence (AI). Furthermore, the pickup error cause determination unit may be configured to acquire the cause of a pickup error and/or the cause of a possible pickup error and how to deal with it by inputting the time-series pressure data into a trained learning model that has undergone machine learning using previously collected multiple time-series pressure data when a pickup error occurred and the causes of the pickup error and/or the causes of the possible pickup error and how to deal with them as training data. In this way, using a machine learning model for pattern-based determination enables even more accurate determination of the cause of a pickup error and how to deal with it.

The cause of the pickup error and how to deal with it can be displayed on the display screen or output as audio. This allows the operator to quickly address the cause.

1 is a block diagram showing the main components of a configuration of a molded product removal machine according to an embodiment of the present invention. 10A and 10B are diagrams used to explain a movement path of a take-out head caused by a movement mechanism. FIG. 10 is a diagram illustrating an example of configuring part of the function of the pickup error cause determination unit using a learning model. (A) is a typical time-series change pattern of the suction pressure when no suction error has occurred, and (B) to (E) are examples of time-series change patterns of the suction pressure when a suction error has occurred or when there is a high possibility of a suction error occurring. 5 is a table summarizing examples of events, causes, and items to be checked (measures) in the case of the time-series change patterns of FIGS. 4B to 4E.

An embodiment of the molded product removal machine of the present invention will be described in detail below with reference to the drawings. FIG. 1 is a block diagram showing the main components of a molded product removal machine 1 according to this embodiment of the present invention, which removes molded products from a molding machine 11. The molded product removal machine 1 includes a vacuum suction device 3, a movement mechanism 5, and a control device 7. The configuration in FIG. 1 illustrates the arrangement of a pressure valve 34 used when a vacuum pump is used as the vacuum suction device 3. When a vacuum ejector is used as the vacuum generator 33, the pressure valve 34 is located in front of the vacuum generator 33. Therefore, the present invention is not limited to the configuration in FIG. 1. The vacuum suction device 3 includes a suction member 31 consisting of a suction nozzle that suctions the molded product, a vacuum generator 33 connected to the suction member 31 via a pipe 32 and applying vacuum pressure to the suction member 31, a pressure valve 34 that opens or closes communication between the vacuum generator 33 and the suction member 31, and a pressure detector 35 that detects the pressure in the pipe 32 between the pressure valve 34 and the suction member 31. The suction member 31 is attached to the take-out head 8, which is moved by the movement mechanism 5.

When the movement mechanism 5 is a well-known three-axis movement mechanism, i.e., an XYZ-type movement mechanism, as shown in Figure 2, during the removal operation to remove a molded product, the removal head 8 equipped with the suction member 31 moves along the Z axis and Y axis in the following order: standby position (position where descent begins) → descent position → removal position → extraction position → raised position. At the removal position, the movement mechanism 5 uses the suction member 31 to suck and remove the molded product pushed out of the molding mold 12 of the molding machine 11 by the protruding action of the ejector pins driven by the ejector pin drive mechanism 13. The movement mechanism 5 then moves the removal head 8 equipped with the suction member 31 from the raised position to an open position (not shown). At the open position, suction by the suction member 31 is released, and the molded product MP is released at the open position.

The control device 7 controls the operation of the vacuum generator 33 and pressure valve 34 and the drive of the movement mechanism 5. As shown in FIG. 1, in this embodiment, the control device 7 at least includes a movement mechanism control unit 71 that controls the movement mechanism 5, a vacuum generator control unit 72 that controls the vacuum generator 33, a control device 7 that controls the operation of the vacuum generator 33 and pressure valve 34 and the drive of the movement mechanism 5, and a suction failure monitoring unit 73 that monitors for suction failures of the suction member 31 to suction the molded product based on the output of the pressure detector 35. Of course, the control device 7 may include other components if necessary to reduce air consumption.

The suction error monitoring unit 73 includes a pressure data storage unit 73A and a suction error cause determination unit 73B. The pressure data storage unit 73A stores pressure changes output by the pressure detector 35 as time-series pressure data. The time-series pressure data includes all information regarding the suction status during the suction process and the suction process after suction. Therefore, even if the pressure does not increase or temporarily fluctuates for some reason during the suction operation, these conditions are reflected in the time-series pressure data. The time-series pressure data includes, in time series, the pressure in the piping 32 before the suction member 31 suctions the molded product (dry suction pressure), the suction operation start pressure Pb indicating that the suction member 31 has started suctioning the molded product, the post-extraction rise pressure Pa after the suction member 31 has risen to a predetermined rise position after the suction operation start pressure is detected, and the peak pressure Pp, which is the maximum pressure in the piping 32 between the detection of the dry suction pressure and the detection of the post-extraction rise pressure Pa. Although these pressures are characteristic of adsorption operation, not all of them are clearly included in the time-series pressure data; depending on the situation, they may or may not appear in the pressure data. Time-series judgment criteria data is created based on information such as the presence or absence of these pressures, their magnitude, and the period during which they appear (time-series pressure change pattern information).

The time-series judgment criteria data here includes a reference pressure value determined through prior testing and multiple time-series pressure change pattern information related to the causes of suction errors and causes linked to the possibility of suction errors, which have been collected in advance. The time-series judgment criteria data used by the suction error cause determination unit 73B includes time-series judgment criteria data that, for example, determines that there is no suction error when the post-takeout rise pressure Pa and peak pressure Pp are lower than the reference pressure value Pr, and determines that a suction error has occurred or is likely to occur when this condition is not met. The suction error cause determination unit 73B is configured to output information on the cause of the suction error and/or the cause of the possibility of a suction error based on the comparison result and the multiple time-series pressure change pattern information when it determines that a suction error has occurred or is likely to occur. Using the time-series comparison results, the trends apparent in the comparison results do not change significantly even if the suction head used is changed, preventing erroneous determination of the cause of the suction error and the cause of the possibility of a suction error.

The suction error cause determination unit 73B compares the time-series pressure data stored in the pressure data storage unit 73A with predetermined criteria data, and based on the time-series comparison results, determines and outputs the occurrence of a failure to suction a molded product by the suction member 31, its cause, and the cause of the possibility of a suction error occurring, as well as countermeasures for those causes. Specifically, the suction error cause determination unit 73B used in this embodiment includes a data storage unit 73Ba that stores information such as the reference pressure value and criteria data, and a comparison unit 73Bb and search unit 73Bc that are implemented by a processor.

The time-series criteria data stored in the data storage unit 73Ba within the pickup error cause determination unit 73B includes criteria data that serve as pre-determined criteria based on multiple representative time-series change patterns 1 to 4 in which pickup errors occur or are likely to occur, as shown in Figure 4, the pressure change trends of these change patterns, and time-series pressure data when a pickup error actually occurs. In addition, the data storage unit 73Ba also stores reference pressure values determined through prior testing, as well as previously collected information related to the causes of pickup errors and possible causes of pickup errors, and countermeasures for those causes, in order to determine the cause and how to deal with them.

The pickup error cause determination unit 73B uses the comparison unit 73Bb to compare the time-series pressure data stored in the pressure data storage unit 73A with the time-series criteria data. The comparison unit 73Bb determines whether a pickup error has occurred and whether there is a possibility of a pickup error occurring based on the comparison results. The search unit 73Bc then searches for and outputs the cause of the pickup error, the cause of the possibility of a pickup error occurring, and a solution to the problem based on the comparison results of the comparison unit 73Bb. When the comparison unit 73Bb of the pickup error cause determination unit 73B determines that a pickup error has occurred or is likely to occur, the result is output as an alarm to the movement mechanism control unit 71. The cause and solution found by the search unit 73Bc are displayed on the display device 75 within the control device 7. The display on the display device 75 may be an image or an audible signal.

For example, as shown in Figures 4(A) through 4(E), the suction error cause determination unit 73B may use a reference pressure value Pr determined through prior testing as an example of the determination criteria. In this case, the suction error cause determination unit 73B determines, as a first step in its determination, that a suction error has not occurred if the dry suction pressure is lower than the reference pressure value Pr and the post-extraction rise pressure Pa and peak pressure Pp (described later) are higher than the reference pressure value Pr. If these conditions are not met, the suction error cause determination unit 73B determines that a suction error has occurred. Using the time-series comparison results in this way prevents erroneous determinations of a suction error due to momentary pressure fluctuations, even when the suction head being used is changed. The reference pressure value Pr is a pressure that is recognized based on past test results as the pressure reached in most cases where a suction error has not occurred. It is known that the reference pressure value Pr varies depending on conditions such as the structure of the suction member 31, the length of the piping 32, and the type of vacuum generator 33. Therefore, it is preferable that this reference pressure value Pr be changeable. As this reference pressure value Pr, a first reference pressure value used to determine whether a suction error has occurred and a second reference pressure value used to determine whether a suction error may occur may be prepared separately. In this case, the second reference pressure value is set to a value smaller than the first reference pressure value. It is preferable to adjust the reference pressure value Pr so that it decreases as the length of the piping 32 increases.

In this embodiment, for the second stage of judgment, information such as judgment criteria data is stored in the data storage unit 73Ba of the pick-up error cause judgment unit 73B. As described above, the time-series judgment criteria data includes a reference pressure value determined through prior testing and multiple time-series pressure change pattern information related to the causes of pick-up errors and the causes linked to the possibility of pick-up errors collected in advance. The time-series judgment criteria data for comparison by the comparison unit 73Bb includes, for example, time-series judgment criteria data for determining that there is no pick-up error when the post-take-out rising pressure Pa and peak pressure Pp are lower than the reference pressure value Pr, and for determining that a pick-up error has occurred or is likely to occur when this condition is not met. The data storage unit 73Ba also includes multiple other time-series judgment criteria data for comparison. Based on the comparison results of the comparison unit 73Bb, the search unit 73Bc searches for pick-up error causes or possible pick-up error causes and countermeasures stored in the data storage unit 73Ba and outputs the results to the display device 75.

The pressure data storage unit 73A stores time-series pressure data for each removal cycle. As described above, when the pickup error cause determination unit 73B determines that a pickup error has occurred or that a pickup error is likely to occur, it determines the cause of the pickup error or the cause of the possible pickup error from this time-series change pattern and outputs a solution.

To further improve accuracy, as shown in FIG. 3, a trained learning model 74 constructed through machine learning may be used in the pick-up error cause determination unit 73B. In this case, the time-series pressure data may be input into the trained learning model 74, which has undergone machine learning using previously collected time-series pressure data when a pick-up error occurred, the cause of the pick-up error, and time-series pressure data before the pick-up error occurred, the cause of the pick-up error, and countermeasures for the error as training data, to obtain the cause of the pick-up error or the possible cause of the pick-up error. By using such a trained learning model 74, inputting the time-series pressure data into the learning model 74 makes it possible to obtain whether or not a pick-up error occurred, whether or not a pick-up error is likely to occur, the cause of the error, and countermeasures for the error. Using the learning model 74 to determine the cause of the pick-up error or the possible cause of the pick-up error and the countermeasures for the error based on the change pattern of the time-series pressure data in this way allows for even more accurate determination of the cause of the pick-up error and the countermeasures for the error. The cause of the pick-up error, the possible cause of the pick-up error, and the countermeasures for the error may be displayed on the screen of the display device 75, or may be output as audio from the display device 75. This allows workers to quickly address the cause of the problem.

Figure 4(A) shows a typical change pattern in the time-series pressure data of the suction pressure when no suction error has occurred. In contrast, Figures 4(B) to (E) show representative examples of time-series change patterns of the suction pressure when a suction error has occurred or is highly likely to occur. In the case of time-series change pattern 1, which is also used as the judgment criteria data in Figure 4(B), the time-series waveform of the suction pressure shows almost no change in pressure after reaching the dry suction pressure, even when the suction timing arrives. In such a case, it is possible that the suction member 31 is not in contact with the molded product. When the comparison unit 73Bb outputs as a comparison result that such time-series pressure data has been input to the suction error cause determination unit 73B, the search unit 73Bc determines that the cause is either a teaching error or the installation condition of the suction member 31, and suggests measures such as redoing the teaching or checking the condition of the suction member.

Furthermore, in the case of time-series change pattern 2, which serves as the time-series judgment criteria data shown in Figure 4(C), the suction pressure waveform shows that after reaching the dry suction pressure, the pressure rises once, but then drops before reaching the suction timing. In other words, peak pressure Pp occurs before reaching post-removal rise pressure Pa. If the input time-series pressure data corresponds to this judgment criteria data, there is a possibility that the suction member 31 became detached from the molded product after peak pressure Pp occurred. Therefore, when the comparison unit 73Bb outputs as a comparison result that such time-series pressure data has been input to the suction error cause judgment unit 73B, the search unit 73Bc determines that the cause is incorrect teaching, a problem with the vacuum generator 33, or the installation of the suction member 31, and suggests the following remedies: redoing the teaching, checking the settings of the vacuum generator 33, or checking the condition of the suction member.

Furthermore, in the case of time-series change pattern 3, which serves as the time-series judgment reference data in Figure 4(D), the suction pressure waveform rises after reaching the dry suction pressure, and its shape is similar to the normal pattern in Figure 4(A). However, in this time-series change pattern, the pressure at the suction timing does not reach the reference pressure value Pr. In such a case, a suction error has occurred or there is a possibility that a suction error will occur in the near future. Therefore, when the comparison unit 73Bb outputs as a comparison result that such time-series pressure data has been input to the suction error cause judgment unit 73B, the search unit 73Bc determines that the cause is incorrect teaching, a problem with the vacuum generator 33, a problem with the installation of the suction member 31, or a problem with the setting of the reference pressure value Pr, and suggests the following remedies: redoing the teaching, checking the setting conditions of the vacuum generator 33, checking the condition of the suction member, or checking for an error in the setting of the reference pressure value Pr.

Furthermore, in the case of time-series change pattern 4, which serves as the time-series judgment reference data in Figure 4(E), the suction pressure waveform shows a rise in pressure after reaching the dry suction pressure, but the pressure continues to rise even after the suction timing is reached. The pressure at the suction timing has not yet reached the reference pressure value Pr, and then exceeds the reference pressure value Pr, becoming saturated. In such a case, although the molded product is being suctioned by the suction member 31, it may not be suctioned sufficiently. In other words, when such time-series pressure data is input to the suction error cause determination unit 73B, the comparison unit 73Bb determines that a suction error may have occurred. The display device 75 then displays a message indicating that the cause is an incorrect suction timing setting, and the search unit 73Bc suggests redoing the teaching as a remedy. It is also effective to suggest that the vacuum generator 33 or the suction member 31 needs to be checked as a remedy.

Figure 5 summarizes examples of events, causes, and countermeasures to be checked for time series change patterns 1 to 4, which serve as the reference data for the time series shown in Figures 4(B) to (E). The learning model 74 in Figure 3 is constructed using this data as training data.

In this embodiment, the pickup error cause determination unit 73B displays the cause and solution on the display screen of the display device or notifies the worker by voice based on the determination result. This has the advantage of making it easier for the worker to take action after discovering a pickup error.

For example, to further improve accuracy, as shown in FIG. 3, a trained learning model 74 constructed through machine learning may be used in the pick-up error cause determination unit 73B. In this case, the cause of a pick-up error may be obtained by inputting time-series pressure data into the trained learning model 74, which has undergone machine learning using previously collected multiple time-series pressure data when a pick-up error occurred, the cause of the pick-up error, and previously collected multiple time-series pressure data when a pick-up error was likely to occur, as training data. In this case, by using the trained learning model 74, which has undergone machine learning using previously collected multiple time-series pressure data when a pick-up error occurred, the cause of the pick-up error, and countermeasures, as training data, inputting the time-series pressure data into the learning model 74, it is possible to obtain whether a pick-up error occurred, whether a pick-up error is likely to occur, the cause of the pick-up error, and countermeasures. In this way, using the learning model 74 to determine the cause of a pick-up error or the possible cause of a pick-up error and the countermeasures, based on the change pattern of the time-series pressure data, allows for even more accurate determination of the cause of a pick-up error and the countermeasures. The causes of pickup errors, possible causes of pickup errors, and how to deal with them may be displayed on the screen of the display device 75, or may be output as audio from the display device 75. This allows the operator to quickly address the cause.

The time-series judgment criteria data and data on causes of pickup errors and how to deal with them shown in Figures 4 and 5 form part of the training data when creating the learning model 74 shown in Figure 3. However, when creating an actual learning model, as much data as possible from the use of multiple types of pick-up heads and multiple types of pickup members is collected and used as training data.

As with the present invention, by comparing time-series pressure data with predetermined time-series judgment criteria data and basing the results of the time-series comparison on the results, even if the threshold value is temporarily exceeded, it is not possible to erroneously determine that a pickup error has not occurred. Furthermore, the pattern of change in the time-series pressure data makes it possible to detect the occurrence of some cause that could lead to a pickup error during operation. By understanding the characteristics of pressure changes that appear in the time-series pressure data when a pickup error occurs due to a known cause, it is possible to determine the cause of the pickup error or the possibility of a pickup error occurring. Therefore, according to the present invention, it is possible to determine the occurrence of a pickup error and/or the possibility of a pickup error of a molded product with greater accuracy than before, and also to identify the cause of the error.

REFERENCE SIGNS LIST 1 Molded product removal machine 3 Vacuum suction device 5 Movement mechanism 7 Control device 8 Removal head 11 Molding machine 12 Molding mold 14 Ejector pin movement mechanism 31 Suction member 32 Piping 33 Vacuum generator 34 Pressure valve 35 Pressure detector 7 Control device 71 Movement mechanism control unit 72 Vacuum generator control unit 73 Suction failure monitoring unit 73A Pressure data storage unit 73B Suction failure cause determination unit

Claims (10)

a vacuum suction device including a suction member that suctions a molded product, a vacuum generator connected to the suction member via a pipe and applying vacuum pressure to the suction member, a pressure valve that controls the vacuum pressure applied from the vacuum generator to the suction member, and a pressure detector that detects the pressure acting on the suction member;
a moving mechanism that moves the suction member to remove the molded product from the molding die of the molding machine;
a control device that controls the operation of the vacuum generating device and the pressure valve and the drive of the moving mechanism;
a pressure data storage unit that stores the change in pressure detected by the pressure detector as time-series pressure data;
a suction error cause determination unit that determines whether a suction error of the molded product by the suction member has occurred and/or whether a suction error may occur, and the cause thereof,
The suction error cause determination unit determines, based on a time-series comparison result obtained by comparing the time-series pressure data with predetermined time-series judgment reference data, that a suction error has occurred in the suction member to suction the molded product and /or that a suction error may occur, as well as the cause of the suction error. The molded product removal machine of claim 1, wherein the time-series pressure data includes the dry suction pressure, which is the pressure in the pipe before the suction member suctions the molded product; the suction operation start pressure, which indicates that the suction member has started suctioning the molded product; the post-removal rise pressure, which is the maximum pressure in the pipe between the detection of the dry suction pressure and the detection of the post-removal rise pressure. the time-series judgment reference data includes a reference pressure value determined by a previous test and a plurality of time-series pressure change pattern information related to causes of suction errors collected in advance,
3. The molded product take-out machine according to claim 2, wherein the suction error cause determination unit determines that there is no suction error under conditions that the dry suction pressure is lower than the reference pressure value and that the post-take-out rise pressure and the peak pressure are lower than the reference pressure values, and determines that a suction error has occurred or is likely to occur when these conditions are not satisfied, and when it determines that a suction error has occurred or is likely to occur, outputs information on the cause of the suction error and/or the cause of the possibility of the suction error based on the comparison result and the plurality of pieces of time- series pressure change pattern information. The molded product remover according to claim 3, wherein the reference pressure value is changeable. A molded product remover according to claim 4, wherein the reference pressure values include a first reference pressure value used to determine whether the suction error has occurred and a second reference pressure value used to determine whether the suction error may occur. the pressure data storage unit stores the pressure data for each take-out cycle;
6. The molded product remover according to claim 1, wherein the suction error cause determination unit outputs a countermeasure for the cause when it determines that the suction error has occurred or that the suction error may occur and the cause of the suction error. The molded product removal machine according to claim 4, wherein the pickup error cause determination unit inputs the time-series pressure data into a trained learning model that has undergone machine learning using multiple time-series pressure data collected in advance when a pickup error occurred and/or when there was a possibility of a pickup error occurring, and the causes of the pickup error as training data, thereby obtaining from the learning model that a pickup error has occurred and/or there is a possibility of a pickup error occurring, as well as the causes thereof. The molded product removal machine according to claim 4, wherein the pickup error cause determination unit inputs multiple time-series pressure data collected in advance when a pickup error occurred and/or when it was determined that a pickup error was likely, and the causes and/or possible causes of pickup errors and countermeasures into a trained learning model that has undergone machine learning using the time-series pressure data as training data, thereby obtaining the cause of the pickup error and countermeasures from the learning model. The molded product remover according to claim 6, wherein the cause of the suction error or the possible cause of the suction error and how to deal with it are displayed on the screen of a display device. The molded product removing machine according to claim 6, wherein the cause of the suction error or the possible cause of the suction error and a solution to the problem are output by voice from a display device.